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Spatio-temporal feature extraction in sensory electroneurographic signals

Lookup NU author(s): Dr Emma Brunton, Professor Kianoush Nazarpour



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


The recording and analysis of peripheral neural signal can provide insight for various prosthetic and bioelectronics medicine applications. However, there are few studies that investigate how informative features can be extracted from population activity electroneurographic (ENG) signals. In this study, five feature extraction frameworks were implemented on sensory ENG datasets and their classification performance was compared. The datasets were collected in acute rat experiments where multi-channel nerve cuffs recorded from the sciatic nerve in response to proprioceptive stimulation of the hindlimb. A novel feature extraction framework, which incorporates spatio-temporal focus and dynamic time warping, achieved classification accuracies above 90% while keeping a low computational cost. This framework outperformed the remaining frameworks tested in this study and has improved the discrimination accuracy of the sensory signals. Thus, this study has extended the tools available to extract features from sensory population activity ENG signals. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'.

Publication metadata

Author(s): Silveira C, Khushaba RN, Brunton E, Nazarpour K

Publication type: Article

Publication status: Published

Journal: Philosophical Transactions. Series A: Mathematical, Physical, and Engineering Sciences

Year: 2022

Volume: 380

Issue: 2228

Print publication date: 25/07/2022

Online publication date: 06/06/2022

Acceptance date: 08/11/2021

Date deposited: 23/06/2023

ISSN (print): 1364-503X

ISSN (electronic): 1471-2962

Publisher: Royal Society Publishing


DOI: 10.1098/rsta.2021.0268

PubMed id: 35658682


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